Recently progress has also been made scaling style transfer methods to higher resolution inputs and outputs. While most style transfer algorithms can at best produce results at 1k resolution, the typical standard for high quality art prints is 6k resolution (6000 pixels on the long side). Wang et al. [1]} approach this through model distillation, proposing a two stage procedure where first the small encoder is distilled with a frozen full size decoder, then the small decoder is learned to accommodate the frozen distilled encoder. Texler et al. [2]} take a different approach, proposing to combine initialization produced by neural style transfer with efficient path-based super-resolution to generate outputs of extremely high resolution (this approach can be combined with all of the algorithms presented in this work, and we provide an example of combining it with NNST in Section ).
